The identification of mental load has important practical significance for human life. In recent years, studies have used physiological signals of different modalities to measure mental load. However, there is no single indicator that can fully assess mental load, and the fusion of multimodal signals to measure this variable is still controversial. The emergence of simultaneous EEG and fMRI enables researchers to explore brain function, especially working memory, with high temporal and spatial resolution. In this study, we fused EEG and fMRI data on network features to estimate working memory load. By introducing a filter bank, the phase synchronization values covering the full frequency band were extracted as the EEG network features. Then, we proposed the Wasserstein distance to measure functional connectivity in single-trial fMRI data. Finally, Fisher vector was used to fuse these features from the above two modalities, and this method was compared with the direct splicing features. The results showed that the Fisher vector was more effective for recognition than direct splicing was, providing a new option for multimodality feature fusion. In addition, distance-based functional connectivity extraction on the individual level enriches the range of tools with which to study brain network.